A spike-timing pattern based neural network model for the study of memory dynamics.

Liu JK, She ZS - PLoS ONE (2009)

Bottom Line:
We show that the distance measure can capture the timing difference of memory states.In addition, we examine the influence of network topology on learning ability, and show that local connections can increase the network's ability to embed more memory states.Together theses results suggest that the proposed system based on spike-timing patterns gives a productive model for the study of detailed learning and memory dynamics.

Affiliation: Department of Mathematics, University of California Los Angeles, Los Angeles, California, United States of America. liujk@ucla.edu

ABSTRACTIt is well accepted that the brain's computation relies on spatiotemporal activity of neural networks. In particular, there is growing evidence of the importance of continuously and precisely timed spiking activity. Therefore, it is important to characterize memory states in terms of spike-timing patterns that give both reliable memory of firing activities and precise memory of firing timings. The relationship between memory states and spike-timing patterns has been studied empirically with large-scale recording of neuron population in recent years. Here, by using a recurrent neural network model with dynamics at two time scales, we construct a dynamical memory network model which embeds both fast neural and synaptic variation and slow learning dynamics. A state vector is proposed to describe memory states in terms of spike-timing patterns of neural population, and a distance measure of state vector is defined to study several important phenomena of memory dynamics: partial memory recall, learning efficiency, learning with correlated stimuli. We show that the distance measure can capture the timing difference of memory states. In addition, we examine the influence of network topology on learning ability, and show that local connections can increase the network's ability to embed more memory states. Together theses results suggest that the proposed system based on spike-timing patterns gives a productive model for the study of detailed learning and memory dynamics.

pone-0006247-g001: Spatiotemporal activity patterns are developed during the learning.(A) STPs are induced by stimulus A (green) and B (yellow) in the same coordinated neural space sorted in the ascending order of E-cells' firing time with respect to stimulus A. The inset shows that both average firing rates are convergent, . (B) State vectors and induced by stimulus A and B, respectively, are in the same neural space formed by ; (c) Learning time is defined as the minimal time such that the normalized distance d falls below the horizonal white line . Here for stimulus A(B), respectively.

Mentions:
The current network has a feature that an unique stable trajectory of E-cells can emerge at the end of a learning process (, where throughout the whole study). In this final state, every E-cell fired only once within one period [Figure 1(A), trajectory A (green) induced by stimulus A ()], and and synaptic weights reached a steady state, for E-cells as in Eq. 10.

pone-0006247-g001: Spatiotemporal activity patterns are developed during the learning.(A) STPs are induced by stimulus A (green) and B (yellow) in the same coordinated neural space sorted in the ascending order of E-cells' firing time with respect to stimulus A. The inset shows that both average firing rates are convergent, . (B) State vectors and induced by stimulus A and B, respectively, are in the same neural space formed by ; (c) Learning time is defined as the minimal time such that the normalized distance d falls below the horizonal white line . Here for stimulus A(B), respectively.

Mentions:
The current network has a feature that an unique stable trajectory of E-cells can emerge at the end of a learning process (, where throughout the whole study). In this final state, every E-cell fired only once within one period [Figure 1(A), trajectory A (green) induced by stimulus A ()], and and synaptic weights reached a steady state, for E-cells as in Eq. 10.

Bottom Line:
We show that the distance measure can capture the timing difference of memory states.In addition, we examine the influence of network topology on learning ability, and show that local connections can increase the network's ability to embed more memory states.Together theses results suggest that the proposed system based on spike-timing patterns gives a productive model for the study of detailed learning and memory dynamics.

Affiliation:
Department of Mathematics, University of California Los Angeles, Los Angeles, California, United States of America. liujk@ucla.edu

ABSTRACTIt is well accepted that the brain's computation relies on spatiotemporal activity of neural networks. In particular, there is growing evidence of the importance of continuously and precisely timed spiking activity. Therefore, it is important to characterize memory states in terms of spike-timing patterns that give both reliable memory of firing activities and precise memory of firing timings. The relationship between memory states and spike-timing patterns has been studied empirically with large-scale recording of neuron population in recent years. Here, by using a recurrent neural network model with dynamics at two time scales, we construct a dynamical memory network model which embeds both fast neural and synaptic variation and slow learning dynamics. A state vector is proposed to describe memory states in terms of spike-timing patterns of neural population, and a distance measure of state vector is defined to study several important phenomena of memory dynamics: partial memory recall, learning efficiency, learning with correlated stimuli. We show that the distance measure can capture the timing difference of memory states. In addition, we examine the influence of network topology on learning ability, and show that local connections can increase the network's ability to embed more memory states. Together theses results suggest that the proposed system based on spike-timing patterns gives a productive model for the study of detailed learning and memory dynamics.